Automatic pre-treatment validation in radiotherapy by computer aided 3D-2D image alignment
type: CreativeWork
description: Most cancer patients may need external beam radiotherapy at certain times. Sufficient exposure of malignant cells and better avoidance of surrounding healthy tissues are necessary in order to minimise damage to non-cancerous regions. Therefore, accurate patient positioning and proper radiation directions as well as dose distributions are crucial. A vital part of external beam radiotherapy is the geometric verification of treatment delivery. This thesis extensively studied three-dimensional (31)) to two-dimensional (2D) image registration methods. The author has proposed two novel methods to register computerised tomography (CT) based volume image data acquired for treatment planning with orthogonal planar images acquired at treatment simulation, which are able to recover the transformation errors in full six degrees of freedom (three translations and three rotations). The first proposed method is a novel feature-based method. It is based on automatic or semi-automatic extraction of object contours from the orthogonal X-ray images, matching with the 3D contours automatically extracted from the planning CT data. The registration is based on the Z-bnffer projection algorithm and the iterative closest point (ICP) algorithm. The novelty of the method proposed by the author is that: with the depth information of the projected features from the 3D model retained by the Z-bnffer method and the correspondence points found by ICP method, the 2D correspondence points are back projected to 3D space. Then the 3D-2D registration can be solved as a 3D-3D registration problem which enables the cost function to be easily built and optimised. The proposed method has been evaluated using simulated data as well as phantom data. For the simulated data, the root-mean-square (RMS) registration errors were 0.70 mm + 0.21 mm for translations and 0.49° + 0.46° for rotations with the capture range up to 18 mm (measured by mean target registration error (mTRE)). For the phantom data, the align…
abstract: Most cancer patients may need external beam radiotherapy at certain times. Sufficient exposure of malignant cells and better avoidance of surrounding healthy tissues are necessary in order to minimise damage to non-cancerous regions. Therefore, accurate patient positioning and proper radiation directions as well as dose distributions are crucial. A vital part of external beam radiotherapy is the geometric verification of treatment delivery. This thesis extensively studied three-dimensional (31)) to two-dimensional (2D) image registration methods. The author has proposed two novel methods to register computerised tomography (CT) based volume image data acquired for treatment planning with orthogonal planar images acquired at treatment simulation, which are able to recover the transformation errors in full six degrees of freedom (three translations and three rotations). The first proposed method is a novel feature-based method. It is based on automatic or semi-automatic extraction of object contours from the orthogonal X-ray images, matching with the 3D contours automatically extracted from the planning CT data. The registration is based on the Z-bnffer projection algorithm and the iterative closest point (ICP) algorithm. The novelty of the method proposed by the author is that: with the depth information of the projected features from the 3D model retained by the Z-bnffer method and the correspondence points found by ICP method, the 2D correspondence points are back projected to 3D space. Then the 3D-2D registration can be solved as a 3D-3D registration problem which enables the cost function to be easily built and optimised. The proposed method has been evaluated using simulated data as well as phantom data. For the simulated data, the root-mean-square (RMS) registration errors were 0.70 mm + 0.21 mm for translations and 0.49° + 0.46° for rotations with the capture range up to 18 mm (measured by mean target registration error (mTRE)). For the phantom data, the align…
author: Chen, Xin
language: en
keywords: I190 - Computer science not elsewhere classified
date published: 2008-03
date modified: 2026-07-24T01:36:20Z
provider: University of Central Lancashire
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: University of Central Lancashire